Human Brain vs Machine Learning

Human Brain vs Machine Learning

Human Brain vs Machine Learning
Human (or any other animal for that matter) brain computational power is limited by two basic evolution requirements : survival and procreation. Our “hardware” (physiology) and “software” (hard-coded nature psychology) only had to evolve to allow us to perform a set of basic actions – identify Friend or Foe, obtain food, find our place in the social tribe hierarchy, ultimately find a mate and multiply. Anything beyond this point, or not directly leading to this point can be considered redundant, when viewed from the evolution perspective. To accomplish these “life” goals, our brains evolved to a certain physical limit (100 billion neurons per average brain, on average 7000 synaptic connections per neuron). Obviously, evolving beyond this limit was not beneficiary for survival and procreation in the African savannas. So, we are hard-limited by our “hardware”, with the hardware spec being 1.5 million years old.

Though, according to the saying, we all “Live and Learn” – we actually live for a relatively short period of time, and learn effectively for an even shorter period. So, the “training set” that each of us has been exposed to in his infancy and childhood is limited by time. Of course, we continue learning things and acquiring skills after we become teens and then adults – but at a much lower, if not negligible, efficiency. We may taste a few exotic fruits, see a new place, study a math subject or try to learn tango – but the truth is that we learned most of our necessary survival skills (telling a person from a tree from a lion etc.) by the age of 3. So, our brain’s “training set” is effectively limited in volume, and this limit is set by all the things we managed to see and do while we were infants, plus a long tail of things we picked up as adults.

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